SBIR Phase I: Novel Spatial Speech Separation Techniques to Improve Speaker Identification and Speech Recognition

SBIR 第一阶段：改进说话人识别和语音识别的新型空间语音分离技术

• 批准号: 0810012
• 负责人: Chiman Kwan
• 金额: $ 10万
• 依托单位: Signal Processing, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0810012&HistoricalAwards=false
• 关键词: SBIR; Phase; Novel; Spatial; Speech

This Small Business Innovation Research (SBIR) Phase I research project focuses on a novel approach that uses multiple microphones to spatially separate speech and hence enhance speaker identification and speech recognition accuracy. The first component of the project will apply feature-driven adaptive beam forming to improve speech recognition accuracy in noisy and reverberant environments. Unlike conventional beam forming methods, this proposed method performs weight adaptation based on feedback from the feature extraction and recognition modules. The arrays can eliminate directional interferences and reduce reverberant effects. For the second component of the proposed project, a new continuously-variable mask estimation method will be used to estimate which parts of the separated speech contain speech with good signal-to noise ratio (SNR), applying speech reconstruction to repair these regions. The repaired speech will then be fed into the speech recognition/speaker identification engine. Thirdly, the project will apply ideas motivated by human binaural perception.The potential market for robust speaker identification and speech recognition is projected to be large, ranging from voice dictation in noisy offices, voiceprint verification for financial transactions, biometrics, speech recognition in automated voice response systems, voice-based search for mobile devices, and several others. Additionally there are many military applications in military command, control, and communications segments, where it is important to convey the correct commands to the other recipients.

这个小型企业创新研究（SBIR）第一阶段研究项目的重点是一种新的方法，使用多个麦克风来空间分离语音，从而提高说话人识别和语音识别的准确性。该项目的第一个组成部分将应用特征驱动的自适应波束形成，以提高噪声和混响环境中的语音识别精度。与传统的波束形成方法不同，该方法根据特征提取和识别模块的反馈进行权重自适应。该阵列可以消除方向性干扰，降低混响效应。对于拟议项目的第二个组成部分，将使用一种新的连续可变掩码估计方法来估计分离语音的哪些部分包含具有良好信噪比（SNR）的语音，应用语音重建来修复这些区域。然后，修复后的语音将被输入语音识别/说话者识别引擎。第三，该项目将应用由人类双耳感知激发的想法，预计稳健的说话人识别和语音识别的潜在市场是巨大的，从嘈杂的办公室中的语音听写，金融交易的声纹验证，生物测定，自动语音应答系统中的语音识别，移动的设备的基于语音的搜索等。此外，在军事指挥、控制和通信部分中有许多军事应用，其中向其他接收者传达正确的命令是重要的。